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Takeorder AI: 24/7 Voice AI Agent for Restaurant Operations
Takeorder AI is an intelligent voice concierge designed specifically for restaurants to revolutionize phone-based customer service. Our advanced AI technology handles phone orders, reservations, and customer inquiries automatically around the clock, ensuring your restaurant never misses a call or loses revenue.
Key Features & Capabilities:
Business Benefits:
Advanced Analytics & Management:
The real-time dashboard provides insights on call volumes, popular menu items, customer satisfaction trends, and revenue impact. Easy menu updates, hours management, and voice script customization through a user-friendly interface.
GitHub
takeorder.aitakeorder.ai's answer:
The only voice AI built specifically for restaurants.
Unlike generic chatbots, we handle: - Complex food orders and dietary restrictions - Human-like conversations with 99%+ accuracy - Zero hold times and seamless POS integration - 4 specialized solutions for different restaurant types
takeorder.ai's answer:
Takeorder AI eliminates missed calls that cost restaurants thousands daily.
Key advantages: 1. 30% revenue increase and 50% fewer missed calls 2. 24/7 operation including holidays 3. Restaurant-trained AI vs basic bots 4. Zero technical setup required
takeorder.ai's answer:
Restaurant owners and operators handling 50+ daily calls: - QSRs, pizzerias, drive-thrus, cafรฉs - Ghost kitchens and full-service restaurants - Anyone struggling with phone overwhelm and missed orders - Operators wanting to scale without hiring more staff
takeorder.ai's answer:
Based on our record, GitHub seems to be more popular. It has been mentiond 2476 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
"""Clones a single branch of a GitHub repository into a temporary directory for local scanning.""" Import logging Import shutil Import subprocess Import tempfile Logger = logging.getLogger(__name__) Class RepoFetchError(RuntimeError): """Raised when the target repository/branch cannot be cloned.""" Class RepoFetcher: """Shallow-clones a single branch of a repository so its tests can be scanned... - Source: dev.to / about 13 hours ago
# video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / 1 day ago
Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 6 days ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 14 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 14 days ago
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
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